

import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""0.12.2"""):
	raise Exception("""requires fairseq >= 0.12.2""")
if version.parse(fairseq.__version__) > version.parse("""2"""):
	raise Exception("""requires fairseq < v2""")

logging.set_verbosity_info()
__snake_case				:      Any   		=			logging.get_logger(__name__)

__snake_case				:      Union[str, Any]   		=			"""Hello, World!"""
__snake_case				:      Optional[int]   		=			"""en_XX"""


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__):

			'''simple docstring'''



			a_						:						Union[str, Any] 							= Path("""data_bin""")
			a_						:						Dict 							= FairseqXmodModel.from_pretrained(
			    model_name_or_path=str(Path(a__).parent)  ,	checkpoint_file=Path(a__).name  ,	_name="""xmod_base"""  ,	arch="""xmod_base"""  ,	task="""multilingual_masked_lm"""  ,	data_name_or_path=str(a__)  ,	bpe="""sentencepiece"""  ,	sentencepiece_model=str(Path(a__).parent / """sentencepiece.bpe.model""")  ,	src_dict=str(data_dir / """dict.txt""")  ,	)
			xmod.eval()  # disable dropout
			print(a__)

			a_						:						Tuple 							= xmod.model.encoder.sentence_encoder
			a_						:						Dict 							= XmodConfig(
			    vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings  ,	hidden_size=xmod.cfg.model.encoder_embed_dim  ,	num_hidden_layers=xmod.cfg.model.encoder_layers  ,	num_attention_heads=xmod.cfg.model.encoder_attention_heads  ,	intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim  ,	max_position_embeddings=5_1_4  ,	type_vocab_size=1  ,	layer_norm_eps=1e-5  ,	pre_norm=xmod.cfg.model.encoder_normalize_before  ,	adapter_reduction_factor=getattr(xmod.cfg.model  ,	"""bottleneck"""  ,	2)  ,	adapter_layer_norm=xmod.cfg.model.adapter_layer_norm  ,	adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm  ,	ln_before_adapter=xmod.cfg.model.ln_before_adapter  ,	languages=xmod.cfg.model.languages  ,	)
			if classification_head:
						a_						:						Any 							= xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

			print("""Our X-MOD config:"""  ,	a__)

			a_						:						str 							= XmodForSequenceClassification(a__) if classification_head else XmodForMaskedLM(a__)
			model.eval()

			# Now let's copy all the weights.
			# Embeddings
			a_						:						str 							= xmod_sent_encoder.embed_tokens.weight
			a_						:						Dict 							= xmod_sent_encoder.embed_positions.weight
			a_						:						List[Any] 							= torch.zeros_like(
			    model.roberta.embeddings.token_type_embeddings.weight)  # just zero them out b/c xmod doesn't use them.

			a_						:						Union[str, Any] 							= xmod_sent_encoder.layernorm_embedding.weight
			a_						:						List[Any] 							= xmod_sent_encoder.layernorm_embedding.bias

			for i in range(config.num_hidden_layers):
						# Encoder: start of layer
						a_						:						Optional[int] 							= model.roberta.encoder.layer[i]
						a_						:						str 							= xmod_sent_encoder.layers[i]

						# self attention
						a_						:						Any 							= layer.attention.self
						if not (
						    xmod_layer.self_attn.k_proj.weight.data.shape
						    == xmod_layer.self_attn.q_proj.weight.data.shape
						    == xmod_layer.self_attn.v_proj.weight.data.shape
						    == torch.Size((config.hidden_size, config.hidden_size))
						):
									raise AssertionError("""Dimensions of self-attention weights do not match.""")

						a_						:						int 							= xmod_layer.self_attn.q_proj.weight
						a_						:						Optional[Any] 							= xmod_layer.self_attn.q_proj.bias
						a_						:						Optional[int] 							= xmod_layer.self_attn.k_proj.weight
						a_						:						Optional[int] 							= xmod_layer.self_attn.k_proj.bias
						a_						:						List[Any] 							= xmod_layer.self_attn.v_proj.weight
						a_						:						int 							= xmod_layer.self_attn.v_proj.bias

						# self-attention output
						a_						:						Optional[Any] 							= layer.attention.output
						if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
									raise AssertionError("""Dimensions of self-attention output weights do not match.""")
						a_						:						Optional[Any] 							= xmod_layer.self_attn.out_proj.weight
						a_						:						Tuple 							= xmod_layer.self_attn.out_proj.bias
						a_						:						int 							= xmod_layer.self_attn_layer_norm.weight
						a_						:						Dict 							= xmod_layer.self_attn_layer_norm.bias

						# intermediate
						a_						:						Any 							= layer.intermediate
						if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
									raise AssertionError("""Dimensions of intermediate weights do not match.""")
						a_						:						Dict 							= xmod_layer.fca.weight
						a_						:						Any 							= xmod_layer.fca.bias

						# output
						a_						:						str 							= layer.output
						if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
									raise AssertionError("""Dimensions of feed-forward weights do not match.""")
						a_						:						List[Any] 							= xmod_layer.fca.weight
						a_						:						Tuple 							= xmod_layer.fca.bias
						a_						:						List[str] 							= xmod_layer.final_layer_norm.weight
						a_						:						List[str] 							= xmod_layer.final_layer_norm.bias
						if bert_output.adapter_layer_norm is not None:
									a_						:						Optional[Any] 							= xmod_layer.adapter_layer_norm.weight
									a_						:						int 							= xmod_layer.adapter_layer_norm.bias

						if sorted(bert_output.adapter_modules.keys()) != sorted(xmod_layer.adapter_modules.keys()):
									raise AssertionError("""Lists of language adapters do not match.""")
						for lang_code, adapter in xmod_layer.adapter_modules.items():
									a_						:						str 							= bert_output.adapter_modules[lang_code]
									a_						:						Dict 							= xmod_layer.adapter_modules[lang_code]
									a_						:						Tuple 							= from_adapter.fca.weight
									a_						:						List[str] 							= from_adapter.fca.bias
									a_						:						Optional[int] 							= from_adapter.fca.weight
									a_						:						Any 							= from_adapter.fca.bias

						# end of layer

			if xmod_sent_encoder.layer_norm is not None:
						a_						:						int 							= xmod_sent_encoder.layer_norm.weight
						a_						:						int 							= xmod_sent_encoder.layer_norm.bias

			if classification_head:
						a_						:						List[str] 							= xmod.model.classification_heads["""mnli"""].dense.weight
						a_						:						Optional[int] 							= xmod.model.classification_heads["""mnli"""].dense.bias
						a_						:						List[Any] 							= xmod.model.classification_heads["""mnli"""].out_proj.weight
						a_						:						Optional[int] 							= xmod.model.classification_heads["""mnli"""].out_proj.bias
			else:
						# LM Head
						a_						:						Union[str, Any] 							= xmod.model.encoder.lm_head.dense.weight
						a_						:						int 							= xmod.model.encoder.lm_head.dense.bias
						a_						:						Optional[int] 							= xmod.model.encoder.lm_head.layer_norm.weight
						a_						:						Any 							= xmod.model.encoder.lm_head.layer_norm.bias
						a_						:						str 							= xmod.model.encoder.lm_head.weight
						a_						:						Any 							= xmod.model.encoder.lm_head.bias

			# Let's check that we get the same results.
			a_						:						List[str] 							= xmod.encode(a__).unsqueeze(0)  # batch of size 1
			model.roberta.set_default_language(a__)

			a_						:						Optional[Any] 							= model(a__)[0]
			if classification_head:
						a_						:						Any 							= xmod.model.classification_heads["""mnli"""](xmod.extract_features(a__))
			else:
						a_						:						Optional[Any] 							= xmod.model(a__  ,	lang_id=[SAMPLE_LANGUAGE])[0]
			print(our_output.shape  ,	their_output.shape)
			a_						:						List[str] 							= torch.max(torch.abs(our_output - their_output)).item()
			print(f'''max_absolute_diff = {max_absolute_diff}''')  # ~ 1e-7
			a_						:						Optional[int] 							= torch.allclose(a__  ,	a__  ,	atol=1e-3)
			print("""Do both models output the same tensors?"""  ,	"""🔥""" if success else """💩""")
			if not success:
						raise Exception("""Something went wRoNg""")

			Path(a__).mkdir(parents=a__  ,	exist_ok=a__)
			print(f'''Saving model to {pytorch_dump_folder_path}''')
			model.save_pretrained(a__)


if __name__ == "__main__":
	__snake_case				:      List[str]   		=			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--xmod_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
	)
	parser.add_argument(
	    """--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
	)
	__snake_case				:      List[str]   		=			parser.parse_args()
	convert_xmod_checkpoint_to_pytorch(
	    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
	)



from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			__a    :					UNetaDModel
			__a    :					ScoreSdeVeScheduler





			def __init__(  self ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   Union[str, Any]:

							'''simple docstring'''


							super().__init__()
							self.register_modules(unet=lowerCAmelCase__ ,  scheduler=lowerCAmelCase__		)





			@torch.no_grad()
			def __call__(  self ,  lowerCAmelCase__ = 1 ,  lowerCAmelCase__ = 20_00 ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = "pil" ,  lowerCAmelCase__ = True ,  **lowerCAmelCase__ ,  )	->   Union[ImagePipelineOutput, Tuple]:

							'''simple docstring'''


							__lowercase					     =				self.unet.config.sample_size
							__lowercase					     =				(batch_size, 3, img_size, img_size)

							__lowercase					     =				self.unet

							__lowercase					     =				randn_tensor(lowerCAmelCase__ ,  generator=lowerCAmelCase__		) * self.scheduler.init_noise_sigma
							__lowercase					     =				sample.to(self.device		)

							self.scheduler.set_timesteps(lowerCAmelCase__		)
							self.scheduler.set_sigmas(lowerCAmelCase__		)

							for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
											__lowercase					     =				self.scheduler.sigmas[i] * torch.ones(shape[0] ,  device=self.device		)

											# correction step
											for _ in range(self.scheduler.config.correct_steps		):
															__lowercase					     =				self.unet(lowerCAmelCase__ ,  lowerCAmelCase__		).sample
															__lowercase					     =				self.scheduler.step_correct(lowerCAmelCase__ ,  lowerCAmelCase__ ,  generator=lowerCAmelCase__		).prev_sample

											# prediction step
											__lowercase					     =				model(lowerCAmelCase__ ,  lowerCAmelCase__		).sample
											__lowercase					     =				self.scheduler.step_pred(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  generator=lowerCAmelCase__		)

											__lowercase     ,	__lowercase					     =				output.prev_sample, output.prev_sample_mean

							__lowercase					     =				sample_mean.clamp(0 ,  1		)
							__lowercase					     =				sample.cpu().permute(0 ,  2 ,  3 ,  1		).numpy()
							if output_type == "pil":
											__lowercase					     =				self.numpy_to_pil(lowerCAmelCase__		)

							if not return_dict:
											return (sample,)

							return ImagePipelineOutput(images=lowerCAmelCase__		)